Department of Civil, Environmental & Construction Engineering, 4000 Central Florida Blvd, University of Central Florida, Orlando, FL 32816, United States.
Accid Anal Prev. 2010 Mar;42(2):654-66. doi: 10.1016/j.aap.2009.10.012. Epub 2009 Nov 8.
The negative binomial (NB) model has been used extensively by traffic safety analysts as a crash prediction model, because it can accommodate the over-dispersion criterion usually exhibited in crash count data. However, the NB model is still a probabilistic model that may benefit from updating the parameters of the covariates to better predict crash frequencies at intersections. The objective of this paper is to examine the effect of updating the parameters of the covariates in the fitted NB model using a Bayesian updating reliability method to more accurately predict crash frequencies at 3-legged and 4-legged unsignalized intersections. For this purpose, data from 433 unsignalized intersections in Orange County, Florida were collected and used in the analysis. Four Bayesian-structure models were examined: (1) a non-informative prior with a log-gamma likelihood function, (2) a non-informative prior with an NB likelihood function, (3) an informative prior with an NB likelihood function, and (4) an informative prior with a log-gamma likelihood function. Standard measures of model effectiveness, such as the Akaike information criterion (AIC), mean absolute deviance (MAD), mean square prediction error (MSPE) and overall prediction accuracy, were used to compare the NB and Bayesian model predictions. Considering only the best estimates of the model parameters (ignoring uncertainty), both the NB and Bayesian models yielded favorable results. However, when considering the standard errors for the fitted parameters as a surrogate measure for measuring uncertainty, the Bayesian methods yielded more promising results. The full Bayesian updating framework using the log-gamma likelihood function for updating parameter estimates of the NB probabilistic models resulted in the least standard error values.
负二项式（NB）模型已被交通安全分析师广泛用于碰撞预测模型，因为它可以满足碰撞计数数据通常表现出的过离散标准。然而，NB 模型仍然是一种概率模型，通过更新协变量的参数可以更好地预测交叉口的碰撞频率。本文的目的是研究使用贝叶斯更新可靠性方法更新拟合 NB 模型中协变量参数的效果，以更准确地预测 3 腿和 4 腿无信号交叉口的碰撞频率。为此，收集了佛罗里达州奥兰治县 433 个无信号交叉口的数据，并在分析中使用了这些数据。检查了四个贝叶斯结构模型：（1）具有对数伽玛似然函数的非信息先验，（2）具有 NB 似然函数的非信息先验，（3）具有 NB 似然函数的信息先验，以及（4）具有对数伽玛似然函数的信息先验。使用模型有效性的标准度量标准，例如 Akaike 信息准则（AIC）、平均绝对偏差（MAD）、均方预测误差（MSPE）和总体预测准确性，来比较 NB 和贝叶斯模型的预测。仅考虑模型参数的最佳估计值（忽略不确定性），NB 和贝叶斯模型都产生了有利的结果。然而，当考虑拟合参数的标准误差作为衡量不确定性的替代度量时，贝叶斯方法产生了更有希望的结果。使用对数伽玛似然函数对 NB 概率模型的参数估计进行完全贝叶斯更新框架导致了最小的标准误差值。
